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Türei, D.* ; Schaul, J.* ; Palacio-Escat, N.* ; Bohár, B.* ; Bai, Y.* ; Ceccarelli, F.* ; Çevrim, E.* ; Daley, M.* ; Darcan, M.* ; Dimitrov, D.* ; Dogan, T.* ; Domingo-Fernández, D.* ; Dugourd, A.* ; Gábor, A.* ; Gul, L.* ; Hall, B.A.* ; Hoyt, C.T.* ; Ivanova, O.* ; Klein, M. ; Lawrence, T.* ; Mañanes, D.* ; Módos, D.* ; Müller-Dott, S.* ; Ölbei, M.* ; Schmidt, C.* ; Şen, B.* ; Theis, F.J. ; Ünlü, A.* ; Ulusoy, E.* ; Valdeolivas, A.* ; Korcsmáros, T.* ; Saez-Rodriguez, J.*

OmniPath: Integrated knowledgebase for multi-omics analysis.

Nucleic Acids Res., DOI: 10.1093/nar/gkaf1126 (2025)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Analysis and interpretation of omics data largely benefit from the use of prior knowledge. However, this knowledge is fragmented across resources and often is not directly accessible for analytical methods. We developed OmniPath (https://omnipathdb.org/), a database combining diverse molecular knowledge from 168 resources. It covers causal protein-protein, gene regulatory, microRNA, and enzyme-post-translational modification interactions, cell-cell communication, protein complexes, and information about the function, localization, structure, and many other aspects of biomolecules. It prioritizes literature curated data, and complements it with predictions and large scale databases. To enable interactive browsing of this large corpus of knowledge, we developed OmniPath Explorer, which also includes a large language model agent that has direct access to the database. Python and R/Bioconductor client packages and a Cytoscape plugin create easy access to customized prior knowledge for omics analysis environments, such as scverse. OmniPath can be broadly used for the analysis of bulk, single-cell, and spatial multi-omics data, especially for mechanistic and causal modeling.
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Publication type Article: Journal article
Document type Scientific Article
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 0305-1048
e-ISSN 1362-4962
Journal Nucleic Acids Research
Publisher Oxford University Press
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
Grants ropean Bioinformatics Institute (EMBL-EBI)
Imperial College Research Fellowship
imperial college london
Landesinstitut für Bioinformatikinfrastruktur in Baden-Württemberg
DOI 10.1093/nar/gkaf1126
PubMed ID 41251164
Erfassungsdatum 2025-11-19
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